Topics

• Merge
• Filter
• Aggregate by group
• Transform

Datasets

• metrics.xlsx and tickers.xlsx.
  • Download from the course website.
  • Upload in Julius.
• Online data from various sources.

Tickers data

• Ask Julius to read tickers.xlsx.
• Ask Julius how many rows are in the data set and what the column names are.
• Ask Julius what the unique values are in the category and sector columns.

Filter

• Ask Julius to filter the tickers data set on the category column to “Domestic Common Stock” and “Domestic Common Stock Primary Class.” Ask Julius to call this data frame common_stock_tickers.
• Ask Julius how many rows are in the common_stock_tickers.
• Ask Julius to show the first few rows.

Metrics data

• Ask Julius to read metrics.xlsx.
• Ask Julius how many rows are in the data set and what the column names are.
• Ask Julius what the unique values are in the date column.

Merge

• Tell Julius to do an inner merge of common_stock_tickers with the metrics data on the ticker column.
• Ask Julius to show the first few rows.

Sort

• Ask Julius to sort the merged data set on date and to show the first few rows.
• Ask Julius to sort on ticker and date and to show the first few rows.
• Ask Julius to save the data set as an Excel file.

Aggregate by group

• Ask Julius to compute the mean marketcap by date.
• Ask Julius to compute the mean marketcap by sector on the date 2024-03-08.
• Ask Julius to compute the mean marketcap grouped by (date, sector) and to present the results as a two-dimensional table.
• Ask Julius to count the number of tickers grouped by (date, sector) and to present the results as a two-dimensional table.

More aggregation

• Ask Julius to compute the percent change in marketcap by ticker.
• Ask Julius to compute the total marketcap by sector on each date and then to compute the percent change by sector.
• Ask Julius to compute the percent of firms for which pe \(<0\) grouped by (sector, scalemarketcap) and to present the results as a two-dimensional table.

Transform

• Ask Julius to create a new variable that is 1 if pe \(>0\) and 0 otherwise (a dummy variable).
• Ask Julius to group by date and rank by marketcap in descending order at each date and add the ranks as a new column in the data set.
• Ask Julius to group by (date, sector) and calculate the excess of pb over the median pb and add this variable to the data set.

Yahoo Finance

• Daily open, high, low, close, adjusted close, volume
• Income statement, balance sheet, and statement of cash flows for past 5 years
• Current market option data (bid, ask, last price, open interest, implied volatility, …)
• Can get with yfinance library

Yahoo’s Adjusted Closing Prices

• Yahoo’s adjusted closing prices are adjusted for splits and dividends.
• The percent change in the adjusted closing price is the daily close-to-close return including dividends.

Caveat

On ex-dividend days, the percent change in the adjusted closing price is \[\frac{P_{t}}{P_{t-1}-D_t} - 1\] rather than what we might prefer: \[\frac{P_{t} + D_t}{P_{t-1}} - 1\] but this is a minor issue (small difference 4 days a year).

Monthly, Annual, … Returns

If we want returns at a different frequency, for example annual returns, then we can either

• compound the daily returns, or
• downsample the adjusted closing prices to annual data and compute the percent change of the downsampled data.

Example

• Ask Julius to run pip install yfinance==0.1.70 and to import yfinance as yf
• Ask Julius to use yf.download to get adjusted closing prices for SPY for the longest history available.
• Ask Julius to downsample the prices to end-of-month.
• Ask Julius to compute monthly returns as the percent change in the downsampled prices.

Federal Reserve Economic Data

• Ask Julius to use the pandas datareader to get the history of crude oil prices from FRED.
• Ask Julius to get the history of inflation rates from FRED.

Ken French’s Data Library

• Ask Julius to use pandas datareader to get the Fama-French factors from Ken French’s data library.
• Ask Julius to list the datasets on Ken French’s data library and to show the entire list in the chat (you may have to tell Julius to use get_available_datasets).
• Ask Julius to get the 48 industry returns from Ken French’s data library.

Scraping

• Ask Julius to find the constituents of the S&P 100.
• When Julius provides a link, ask Julius to read the table at the link.